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Resistive-random-access-memory (ReRAM) based processing-in-memory $(\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M})$ accelerators show promise in bridging the gap between Internet of Thing devices&#x2019; constrained resources and Convolutional/Deep Neural Networks&#x2019; (CNNs/DNNs&#x2019;) prohibitive energy cost. Specifically, $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerators enhance energy efficiency by eliminating the cost of weight movements and improving the computational density through ReRAM&#x2019;s high density. However, the energy efficiency is still limited by the dominant energy cost of input and partial sum (Psum) movements and the cost of digital-to-analog (D/A) and analog-to-digital (AD) interfaces. In this work, we identify three energy-saving opportunities in $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerators: analog data locality, time-domain interfacing, and input access reduction, and propose an innovative $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerator called TIMELY, with three key contributions: (1) TIMELY adopts analog local buffers (ALBs) within ReRAM crossbars to greatly enhance the data locality, minimizing the energy overheads of both input and Psum movements; (2) TIMELY largely reduces the energy of each single D/A (and AD) conversion and the total number of conversions by using time-domain interfaces (TDIs) and the employed ALBs, respectively; (3) we develop an only-once input read $(\mathrm{O}^{2}\mathrm{I}\mathrm{R})$ mapping method to further decrease the energy of input accesses and the number of D/A conversions. The evaluation with more than 10 CNN/DNN models and various chip configurations shows that, TIMELY outperforms the baseline $\mathrm{R}^{2}\mathrm{P}\mathrm{I}\mathrm{M}$ accelerator, PRIME, by one order of magnitude in energy efficiency while maintaining better computational density (up to $31.2\times$) and throughput (up to $736.6\times$). Furthermore, comprehensive studies are performed to evaluate the effectiveness of the proposed ALB, TDI, and $\mathrm{O}^{2}\mathrm{I}\mathrm{R}$ in terms of energy savings and area reduction.

Timely: Pushing Data Movements And Interfaces In Pim Accelerators Towards Local And In Time Domain